Enterprise adoption of LLMs in agentic workflows is gated by evaluations that span a wide arc of abilities: CRM operations, heterogeneous-document retrieval, personal data understanding, multi-turn planning, tool use, function calling, long-horizon research, and long-context software engineering. These evaluations live today in separate repositories with divergent runners, dependencies, and scoring conventions, which makes cross-benchmark comparison difficult and obscures what a single aggregate number actually represents. We release EGI-BENCH, a unified LLM agent benchmark suite that consolidates eight enterprise facing agent benchmarks (CRMARENA, HERB, PERSONABENCH, USERBENCH, MCP-UNIVERSE, MCPEVAL, BENCHFORCE, LOCOBENCH) behind a single command-line interface with two runtime-budgeted execution modes. On top of the suite we define the Enterprise General Intelligence (EGI) Score, a six-dimensional framework (M1-M6) whose buckets are populated by fine-grained sub-metrics routed from each benchmark rather than by benchmark-level averages. We describe the suite, the sub-metric routing, and the quick/deep budgets, and report results for fourteen frontier and cost-efficient LLMs spanning the GPT, Claude, and Gemini families under quick mode. Top-line EGI ranges from 39.6 (GPT-4o mini) to 65.6 (gpt-5.5), and no single model dominates every capability axis: gpt-5.5 leads task outcome, planning, and retrieval grounding; Claude Sonnet 4.5 leads tool actioning and safety/abstention; and Claude Haiku 4.5 leads engineering quality. Code is available at https://github.com/SalesforceAIResearch/egi-bench.
Table 1: Domain coverage, task count, and M 1 –M 6 enterprise-metric coverage across representative agent/LLM benchmarks. ✓ = the benchmark evaluates this dimension as a core part of its design; ∼ = exercised as part of tasks but not a primary axis; ✗ = outside scope.
Metric
Weight
What it measures
M 1 Task Outcome Accuracy
28%
Did the agent produce the correct business outcome or final answer?
M 2 Tool Actioning Reliability
22%
Correct tool selection, parameters, ordering; no extraneous calls.
M 3 Planning & Workflow
16%
Multi-step and multi-turn execution that is step-efficient.
Abstain when appropriate, flag policy violations, no unsafe actions.
M 6 Engineering & Maintainability
6%
Code quality and structural/process discipline.
Table 2: The six enterprise metrics (M1–M6) and their weights in the EGI score. Weights are inherited from the internal PR #28 proposal and are applied uniformly across the suite.
Figure 1: M 1 –M 6 scores across fourteen LLMs under quick mode. Models are ordered by EGI ; each bucket is a cluster of bars. The M 3 (Planning) dimension is uniformly low across models, while M 2 (Tool Actioning) is the strongest axis for nearly every frontier model. The best M 2 (Tool Actioning, 87.17) and M 5 (Safety & Abstention, 80.28) are held by Claude Sonnet 4.5, while the best M 1 (Task Outcome, 82.95), M 3 (Planning, 30.89), and M 4 (Retrieval & Grounding, 54.86) are all held by gpt-5.5 , illustrating that top-line EGI can mask capability-level specialization.
Figure 4
Appendix figures & tables3 assets
Supplementary material from the paper’s appendix.
Appendix
Benchmark
Domain
Tasks (quick / deep)
Primary metric
CRMArena
CRM operations
9 types × 6 / × 3
Task accuracy
HERB
Enterprise search
80 Qs / full
F1 + content correctness
PersonaBench
Personal data QA
3 people × 2 / × 4 noises
QA accuracy
UserBench
Travel planning
3 envs × 60 / all envs
Micro-avg. reward
MCP-Universe
MCP tool use
1 dom. × 20 / 3 dom.
Sub-eval pass rate
MCPEval
MCP tool use
1 dom. × 50 / 3 dom. × 50
Flex. avg. score
Appendix
Table 4: The eight benchmarks unified under EGI-Bench . Task counts reflect the quick and deep mode profiles defined in benchmark_config.yaml . Full benchmark descriptions are in Section 4 .
CRMArena (PVI), HERB (unanswerable null rate), Benchforce (unsafe-failure gap)
M 6
LoCoBench (software_engineering, code_quality)
Appendix
Table 5: Which benchmarks contribute to each enterprise metric after fine-grained decomposition. Every benchmark feeds at least one bucket and every bucket receives at least two routed sub-metrics; M 6 is the only bucket whose sub-metrics come from a single benchmark ( LoCoBench ).
Benchmark
Sub-metric
Bucket
Rationale
CRMArena
NCR (Case Routing)
M 1
Business action correctness
HTU (Handle Time)
M 1
Analytical task outcome
TCU (Transfer Count)
M 1
Analytical task outcome
TII (Top Issue ID)
M 1
Analytical task outcome
BRI (Best Region ID)
M 1
Analytical task outcome
MTA (Monthly Trend)
M 1
Task outcome
Appendix
Table 6: Fine-grained sub-metric routing. Each row shows a native sub-metric reported by a benchmark, the enterprise metric (M 1 –M 6 ) it is routed into, and a short rationale. No benchmark’s overall score is consumed directly; the routing is the unit of aggregation.